{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CIFAR10 classification using HOG features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load CIFAR10 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded CIFAR10 database with 50000 training and 10000 testing samples\n"
     ]
    }
   ],
   "source": [
    "import myutils\n",
    "\n",
    "raw_data_training, raw_data_testing = myutils.load_CIFAR_dataset(shuffle=False)\n",
    "# raw_data_training = raw_data_training[:5000]\n",
    "\n",
    "class_names = myutils.load_CIFAR_classnames()\n",
    "\n",
    "n_training = len( raw_data_training )\n",
    "n_testing = len( raw_data_testing )\n",
    "print('Loaded CIFAR10 database with {} training and {} testing samples'.format(n_training, n_testing))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Converting to greyscale\n",
    "def rgb2gray(image):\n",
    "    import cv2\n",
    "    return cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "Xdata_training = [ rgb2gray(raw_data_training[i][0]) for i in range(n_training)]\n",
    "Xdata_testing  = [ rgb2gray(raw_data_testing[i][0]) for i in range(n_testing)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look how images looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Few examples after preprocessing\n"
     ]
    },
    {
     "data": {
      "image/png": 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jJso0ih2ZY/phpcjXC/uKrhQGDGf7Xa6PDZhZqKJERKkTIhErogD8nXyegzvTaSWpbYP2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\nfmI98brC/BPLR/hnZ+wj29e+9rWSasyU1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MzzQKt49M/Vsb0fbhXVmXvzqvmvUVETERER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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9ba86cac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# lets choose some random sample of 10 training images\n",
    "examples_id = random.sample(range(n_training), 10)\n",
    "fig, axarr = plt.subplots(2,len(examples_id), figsize=(15,3))\n",
    "for i in range(len(examples_id)):\n",
    "    id = examples_id[i]\n",
    "    axarr[0,i].imshow(raw_data_training[id][0][:,:])\n",
    "    axarr[0,i].axis('off')\n",
    "    axarr[1,i].imshow(Xdata_training[id],cmap='gray')\n",
    "    axarr[1,i].axis('off')\n",
    "print('Few examples after preprocessing')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature extraction\n",
    "We use HOG descriptor from [scikit-image](http://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.hog) library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Configuring HOG descriptor\n",
    "# see http://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.hog\n",
    "\n",
    "# Configuration of HOG descriptor\n",
    "normalize = True          #  True ==> yields a little bit better score\n",
    "                          #  \n",
    "block_norm = 'L2-Hys'     # or 'L1'\n",
    "orientations = 9          # \n",
    "pixels_per_cell = [8, 8]  #  see section 'Additional remarks' for some explanation\n",
    "cells_per_block = [2, 2]  # \n",
    "\n",
    "def extractFeature(img, vis=False):\n",
    "    from skimage.feature import hog\n",
    "    return hog(img, orientations, pixels_per_cell, cells_per_block, block_norm, visualise=vis, transform_sqrt=normalize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization of HOG histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features = 324\n"
     ]
    },
    {
     "data": {
      "image/png": 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r8iSm3E4W42qoGJohnluk3qk+FBt5DPcFpWowbLWZr/R1Rfuw8mZ/8sDfRGKU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\nZFtzjlaKYyolkFLv8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eerx4DeNnpkNLpKb6R6blSmvVfwGXogP+9R1egnPXzq9WOZKOuc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9b75cfcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extracting one sample data\n",
    "nfeatures = extractFeature(Xdata_training[0], vis=False).size\n",
    "print('Number of features = {}'.format(nfeatures))\n",
    "\n",
    "fig, axarr = plt.subplots(3,len(examples_id), figsize=(16,5))\n",
    "for i in range(len(examples_id)):\n",
    "    id = examples_id[i]\n",
    "    axarr[0,i].imshow(raw_data_training[id][0][:,:])\n",
    "    axarr[0,i].axis('off')\n",
    "    axarr[1,i].imshow(Xdata_training[id],cmap='gray')\n",
    "    axarr[1,i].axis('off')\n",
    "    _, hog_vis = extractFeature(Xdata_training[id], vis=True)\n",
    "    axarr[2,i].imshow(hog_vis,cmap='gray')\n",
    "    axarr[2,i].axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# feature extraction\n",
    "import numpy as np\n",
    "X_training = np.array( [ extractFeature(Xdata_training[i], vis=False) for i in range(n_training) ] )\n",
    "y_training = np.array( [ raw_data_training[i][1] for i in range(n_training) ] )\n",
    "\n",
    "X_testing = np.array( [ extractFeature(Xdata_testing[i], vis=False) for i in range(n_testing) ] )\n",
    "y_testing = np.array( [ raw_data_testing[i][1] for i in range(n_testing) ] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Short look at the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_training shape is (50000, 324)\n",
      "y_training shape is (50000,)\n",
      "X_testing shape is (10000, 324)\n",
      "y_testing shape is (10000,)\n"
     ]
    }
   ],
   "source": [
    "print( 'X_training shape is {}'.format( X_training.shape ) )\n",
    "print( 'y_training shape is {}'.format( y_training.shape ) )\n",
    "print( 'X_testing shape is {}'.format( X_testing.shape ) )\n",
    "print( 'y_testing shape is {}'.format( y_testing.shape ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_training data description\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>314</th>\n",
       "      <th>315</th>\n",
       "      <th>316</th>\n",
       "      <th>317</th>\n",
       "      <th>318</th>\n",
       "      <th>319</th>\n",
       "      <th>320</th>\n",
       "      <th>321</th>\n",
       "      <th>322</th>\n",
       "      <th>323</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.129458</td>\n",
       "      <td>0.106072</td>\n",
       "      <td>0.114721</td>\n",
       "      <td>0.131569</td>\n",
       "      <td>0.161359</td>\n",
       "      <td>0.117283</td>\n",
       "      <td>0.096029</td>\n",
       "      <td>0.090866</td>\n",
       "      <td>0.095725</td>\n",
       "      <td>0.117669</td>\n",
       "      <td>...</td>\n",
       "      <td>0.092055</td>\n",
       "      <td>0.105863</td>\n",
       "      <td>0.092281</td>\n",
       "      <td>0.099525</td>\n",
       "      <td>0.118193</td>\n",
       "      <td>0.142161</td>\n",
       "      <td>0.103797</td>\n",
       "      <td>0.086137</td>\n",
       "      <td>0.082177</td>\n",
       "      <td>0.085035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.260191</td>\n",
       "      <td>0.176087</td>\n",
       "      <td>0.201889</td>\n",
       "      <td>0.205719</td>\n",
       "      <td>0.268574</td>\n",
       "      <td>0.178080</td>\n",
       "      <td>0.162503</td>\n",
       "      <td>0.152046</td>\n",
       "      <td>0.148762</td>\n",
       "      <td>0.200865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111476</td>\n",
       "      <td>0.198533</td>\n",
       "      <td>0.122557</td>\n",
       "      <td>0.153237</td>\n",
       "      <td>0.134396</td>\n",
       "      <td>0.240421</td>\n",
       "      <td>0.131419</td>\n",
       "      <td>0.148912</td>\n",
       "      <td>0.111301</td>\n",
       "      <td>0.104691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.027626</td>\n",
       "      <td>0.019484</td>\n",
       "      <td>0.024582</td>\n",
       "      <td>0.033589</td>\n",
       "      <td>0.047433</td>\n",
       "      <td>0.024329</td>\n",
       "      <td>0.015015</td>\n",
       "      <td>0.011955</td>\n",
       "      <td>0.011667</td>\n",
       "      <td>0.021786</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014060</td>\n",
       "      <td>0.025480</td>\n",
       "      <td>0.021826</td>\n",
       "      <td>0.027016</td>\n",
       "      <td>0.040672</td>\n",
       "      <td>0.053705</td>\n",
       "      <td>0.026726</td>\n",
       "      <td>0.017118</td>\n",
       "      <td>0.014958</td>\n",
       "      <td>0.014871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.084444</td>\n",
       "      <td>0.065958</td>\n",
       "      <td>0.072605</td>\n",
       "      <td>0.088453</td>\n",
       "      <td>0.117483</td>\n",
       "      <td>0.074988</td>\n",
       "      <td>0.054199</td>\n",
       "      <td>0.049469</td>\n",
       "      <td>0.056918</td>\n",
       "      <td>0.072738</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056095</td>\n",
       "      <td>0.071211</td>\n",
       "      <td>0.060156</td>\n",
       "      <td>0.066941</td>\n",
       "      <td>0.090785</td>\n",
       "      <td>0.116016</td>\n",
       "      <td>0.069890</td>\n",
       "      <td>0.050390</td>\n",
       "      <td>0.048207</td>\n",
       "      <td>0.052963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.170093</td>\n",
       "      <td>0.145786</td>\n",
       "      <td>0.154047</td>\n",
       "      <td>0.172777</td>\n",
       "      <td>0.204218</td>\n",
       "      <td>0.158513</td>\n",
       "      <td>0.130026</td>\n",
       "      <td>0.124754</td>\n",
       "      <td>0.141140</td>\n",
       "      <td>0.161678</td>\n",
       "      <td>...</td>\n",
       "      <td>0.135231</td>\n",
       "      <td>0.146870</td>\n",
       "      <td>0.129359</td>\n",
       "      <td>0.137813</td>\n",
       "      <td>0.160042</td>\n",
       "      <td>0.186719</td>\n",
       "      <td>0.144154</td>\n",
       "      <td>0.118270</td>\n",
       "      <td>0.115226</td>\n",
       "      <td>0.123913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.067971</td>\n",
       "      <td>9.705519</td>\n",
       "      <td>9.957282</td>\n",
       "      <td>7.209022</td>\n",
       "      <td>16.225216</td>\n",
       "      <td>7.708452</td>\n",
       "      <td>7.708452</td>\n",
       "      <td>5.020748</td>\n",
       "      <td>7.401725</td>\n",
       "      <td>10.042482</td>\n",
       "      <td>...</td>\n",
       "      <td>2.801680</td>\n",
       "      <td>18.070520</td>\n",
       "      <td>7.849949</td>\n",
       "      <td>14.093094</td>\n",
       "      <td>6.322701</td>\n",
       "      <td>24.094100</td>\n",
       "      <td>6.418618</td>\n",
       "      <td>16.958440</td>\n",
       "      <td>8.062340</td>\n",
       "      <td>3.802443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 324 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                0             1             2             3             4    \\\n",
       "count  50000.000000  50000.000000  50000.000000  50000.000000  50000.000000   \n",
       "mean       0.129458      0.106072      0.114721      0.131569      0.161359   \n",
       "std        0.260191      0.176087      0.201889      0.205719      0.268574   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.027626      0.019484      0.024582      0.033589      0.047433   \n",
       "50%        0.084444      0.065958      0.072605      0.088453      0.117483   \n",
       "75%        0.170093      0.145786      0.154047      0.172777      0.204218   \n",
       "max       16.067971      9.705519      9.957282      7.209022     16.225216   \n",
       "\n",
       "                5             6             7             8             9    \\\n",
       "count  50000.000000  50000.000000  50000.000000  50000.000000  50000.000000   \n",
       "mean       0.117283      0.096029      0.090866      0.095725      0.117669   \n",
       "std        0.178080      0.162503      0.152046      0.148762      0.200865   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.024329      0.015015      0.011955      0.011667      0.021786   \n",
       "50%        0.074988      0.054199      0.049469      0.056918      0.072738   \n",
       "75%        0.158513      0.130026      0.124754      0.141140      0.161678   \n",
       "max        7.708452      7.708452      5.020748      7.401725     10.042482   \n",
       "\n",
       "           ...                314           315           316           317  \\\n",
       "count      ...       50000.000000  50000.000000  50000.000000  50000.000000   \n",
       "mean       ...           0.092055      0.105863      0.092281      0.099525   \n",
       "std        ...           0.111476      0.198533      0.122557      0.153237   \n",
       "min        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "25%        ...           0.014060      0.025480      0.021826      0.027016   \n",
       "50%        ...           0.056095      0.071211      0.060156      0.066941   \n",
       "75%        ...           0.135231      0.146870      0.129359      0.137813   \n",
       "max        ...           2.801680     18.070520      7.849949     14.093094   \n",
       "\n",
       "                318           319           320           321           322  \\\n",
       "count  50000.000000  50000.000000  50000.000000  50000.000000  50000.000000   \n",
       "mean       0.118193      0.142161      0.103797      0.086137      0.082177   \n",
       "std        0.134396      0.240421      0.131419      0.148912      0.111301   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.040672      0.053705      0.026726      0.017118      0.014958   \n",
       "50%        0.090785      0.116016      0.069890      0.050390      0.048207   \n",
       "75%        0.160042      0.186719      0.144154      0.118270      0.115226   \n",
       "max        6.322701     24.094100      6.418618     16.958440      8.062340   \n",
       "\n",
       "                323  \n",
       "count  50000.000000  \n",
       "mean       0.085035  \n",
       "std        0.104691  \n",
       "min        0.000000  \n",
       "25%        0.014871  \n",
       "50%        0.052963  \n",
       "75%        0.123913  \n",
       "max        3.802443  \n",
       "\n",
       "[8 rows x 324 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "print( 'X_training data description')\n",
    "pd.DataFrame( X_training ).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_training data description\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.87231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "count  50000.00000\n",
       "mean       4.50000\n",
       "std        2.87231\n",
       "min        0.00000\n",
       "25%        2.00000\n",
       "50%        4.50000\n",
       "75%        7.00000\n",
       "max        9.00000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print( 'y_training data description')\n",
    "pd.DataFrame( y_training ).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA 2D decomposition "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.20490792  0.1022156 ]\n"
     ]
    },
    {
     "data": {
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zwTRa7bzngqNHeToAAPZFTrOEfcD//eED20LuCQPDrfzdNfPTajvtEgCAnYk52AcsWt2/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\nulMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9e9c6f908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import decomposition\n",
    "pca = decomposition.PCA(n_components=2)\n",
    "pca.fit(X_training)\n",
    "X = pca.transform(X_training)\n",
    "print(pca.explained_variance_ratio_)\n",
    "\n",
    "plt.figure( figsize=(15,15) )\n",
    "plt.scatter( X[:, 0], X[:, 1], c=y_training, cmap='tab10' )\n",
    "# plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO: remove outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We simply use *LinearSVC* from [scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC) library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# parameter C chosen experimentally (see explanation below)\n",
    "C = 1.0\n",
    "\n",
    "clf = LinearSVC(C=C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this may take some time\n",
    "clf.fit(X_training, y_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing and score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We obtain **0.4914** score on CIFAR10 testing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49120000000000003"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score( X_testing, y_testing )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Short look at the prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_predict = clf.predict( X_testing )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.unique( y_predict )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our results for different parameters\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "TODO: \n",
    "* show misclassifications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional remarks\n",
    "\n",
    "## Tuning C parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normalize=True, C=0.001, score=0.4758\n",
      "normalize=True, C=0.01, score=0.4857\n",
      "normalize=True, C=0.1, score=0.4904\n",
      "normalize=True, C=1.0, score=0.4917\n",
      "normalize=True, C=1.2, score=0.4908\n",
      "normalize=True, C=1.5, score=0.4908\n",
      "normalize=True, C=2.0, score=0.4908\n",
      "normalize=True, C=10.0, score=0.4848\n"
     ]
    }
   ],
   "source": [
    "for C in [ 0.001, 0.01, 0.1, 1.0, 1.2, 1.5, 2.0, 10.0 ]:\n",
    "    clf = LinearSVC(C=C)\n",
    "    clf.fit(X_training, y_training)\n",
    "    print( 'normalize={norm}, C={C}, score={score}'.format(norm=normalize, C=C, score=clf.score( X_testing, y_testing )) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## SVC(kernel=linear) and SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.51180000000000003"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_lin_clf = SVC(kernel='linear', C=1)\n",
    "svc_lin_clf.fit(X_training, y_training)\n",
    "svc_lin_clf.score(X_testing, y_testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47549999999999998"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc_clf = SVC(C=1)\n",
    "svc_clf.fit(X_training, y_training)\n",
    "svc_clf.score(X_testing, y_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Some results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LinearSVC\n",
    "<pre>\n",
    "normalize=False, C=0.001, score=0.281\n",
    "normalize=False, C=0.01, score=0.3522\n",
    "normalize=False, C=0.1, score=0.4432\n",
    "normalize=False, C=1.0, score=0.4765\n",
    "normalize=False, C=1.2, score=0.4786\n",
    "normalize=False, C=1.5, score=0.4783\n",
    "normalize=False, C=2.0, score=0.4802\n",
    "normalize=False, C=10.0, score=0.4841\n",
    "normalize=False, C=20.0, score=0.4834\n",
    "normalize=False, C=50.0, score=0.4833\n",
    "normalize=False, C=100.0, score=0.4833\n",
    "normalize=False, C=1000.0, score=0.4667\n",
    "\n",
    "normalize=True, C=0.001, score=0.4758\n",
    "normalize=True, C=0.01, score=0.4857\n",
    "normalize=True, C=0.1, score=0.4903\n",
    "normalize=True, C=1.0, score=**0.4913**\n",
    "normalize=True, C=1.2, score=0.4904\n",
    "normalize=True, C=1.5, score=0.4906\n",
    "normalize=True, C=2.0, score=0.491\n",
    "normalize=True, C=10.0, score=0.4902\n",
    "normalize=True, C=20.0, score=0.4913\n",
    "normalize=True, C=50.0, score=0.4222\n",
    "normalize=True, C=100.0, score=0.4166\n",
    "normalize=True, C=1000.0, score=0.3139\n",
    "\n",
    "TODO: better presentation\n",
    "</pre>\n",
    "\n",
    "SVC(kernel='linear')\n",
    "<pre>\n",
    "normalize=True, C=1.0, score=0.5118\n",
    "</pre>\n",
    "\n",
    "SVC()\n",
    "<pre>\n",
    "normalize=True, C=1.0, score=0.4755\n",
    "</pre>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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